---
title: Advanced Retrieval
icon: "magnifying-glass"
iconType: "solid"
description: "Advanced memory search with keyword expansion, intelligent reranking, and precision filtering"
---

## What is Advanced Retrieval?

Advanced Retrieval gives you precise control over how memories are found and ranked. While basic search uses semantic similarity, these advanced options help you find exactly what you need, when you need it.

<CodeGroup>

## Search Enhancement Options

### Keyword Search
**Expands results** to include memories with specific terms, names, and technical keywords.

<Tabs>
  <Tab title="When to Use">
- Searching for specific entities, names, or technical terms
- Need comprehensive coverage of a topic  
- Want broader recall even if some results are less relevant
- Working with domain-specific terminology
</Tab>
<Tab title="How it Works">
```python Python
# Find memories containing specific food-related terms
results = client.search(
    query="What foods should I avoid?",
    keyword_search=True,
    user_id="user123"
)

# Results might include:
# ✓ "Allergic to peanuts and shellfish"  
# ✓ "Lactose intolerant - avoid dairy"
# ✓ "Mentioned avoiding gluten last week"
```
  </Tab>
  <Tab title="Performance">
- **Latency**: ~10ms additional
- **Recall**: Significantly increased
- **Precision**: Slightly decreased
- **Best for**: Entity search, comprehensive coverage
</Tab>
</Tabs>

### Reranking  
**Reorders results** using deep semantic understanding to put the most relevant memories first.

<Tabs>
  <Tab title="When to Use">
- Need the most relevant result at the top
- Result order is critical for your application
- Want consistent quality across different queries
- Building user-facing features where accuracy matters
</Tab>
<Tab title="How it Works">
```python Python
# Get the most relevant travel plans first
results = client.search(
    query="What are my upcoming travel plans?",
    rerank=True,
    user_id="user123"
)

# Before reranking:        After reranking:
# 1. "Went to Paris"   →   1. "Tokyo trip next month"
# 2. "Tokyo trip next" →   2. "Need to book hotel in Tokyo"  
# 3. "Need hotel"      →   3. "Went to Paris last year"
```
</Tab>
<Tab title="Performance">
- **Latency**: 150-200ms additional
- **Accuracy**: Significantly improved
- **Ordering**: Much more relevant
- **Best for**: Top-N precision, user-facing results
</Tab>
</Tabs>

### Memory Filtering
**Filters results** to keep only the most precisely relevant memories.

<Tabs>
<Tab title="When to Use">
- Need highly specific, focused results
- Working with large datasets where noise is problematic  
- Quality over quantity is essential
- Building production or safety-critical applications
</Tab>
<Tab title="How it Works">
```python Python
# Get only the most relevant dietary restrictions
results = client.search(
    query="What are my dietary restrictions?",
    filter_memories=True,
    user_id="user123"
)

# Before filtering:           After filtering:
# • "Allergic to nuts"    →   • "Allergic to nuts"
# • "Likes Italian food"  →   • "Vegetarian diet"
# • "Vegetarian diet"     →   
# • "Eats dinner at 7pm"  →   
```
</Tab>
<Tab title="Performance">
- **Latency**: 200-300ms additional
- **Precision**: Maximized
- **Recall**: May be reduced
- **Best for**: Focused queries, production systems
</Tab>
</Tabs>

## Real-World Use Cases

<Tabs>
<Tab title="Personal AI Assistant">
```python Python
# Smart home assistant finding device preferences
results = client.search(
    query="How do I like my bedroom temperature?",
    keyword_search=True,    # Find specific temperature mentions
    rerank=True,           # Get most recent preferences first
    user_id="user123"
)

# Finds: "Keep bedroom at 68°F", "Too cold last night at 65°F", etc.
```
</Tab>
<Tab title="Customer Support">
```python Python
# Find specific product issues with high precision
results = client.search(
    query="Problems with premium subscription billing",
    keyword_search=True,     # Find "premium", "billing", "subscription"
    filter_memories=True,    # Only billing-related issues
    user_id="customer456"
)

# Returns only relevant billing problems, not general questions
```
</Tab>
<Tab title="Healthcare AI">
```python Python
# Critical medical information needs perfect accuracy
results = client.search(
    query="Patient allergies and contraindications",
    rerank=True,            # Most important info first
    filter_memories=True,   # Only medical restrictions
    user_id="patient789"
)

# Ensures critical allergy info appears first and filters out non-medical data
```
</Tab>
<Tab title="Learning Platform">
```python Python
# Find learning progress for specific topics
results = client.search(
    query="Python programming progress and difficulties",
    keyword_search=True,    # Find "Python", "programming", specific concepts
    rerank=True,           # Recent progress first
    user_id="student123"
)

# Gets comprehensive view of Python learning journey
```
</Tab>
</Tabs>

## Choosing the Right Combination

### Recommended Configurations

<CodeGroup>
```python Python
# Fast and broad - good for exploration
def quick_search(query, user_id):
    return client.search(
        query=query,
        keyword_search=True,
        user_id=user_id
    )

# Balanced - good for most applications  
def standard_search(query, user_id):
    return client.search(
        query=query,
        keyword_search=True,
        rerank=True,
        user_id=user_id
    )

# High precision - good for critical applications
def precise_search(query, user_id):
    return client.search(
        query=query,
        rerank=True,
        filter_memories=True,
        user_id=user_id
    )
```

```javascript JavaScript
// Fast and broad - good for exploration
function quickSearch(query, userId) {
    return client.search(query, {
        user_id: userId,
        keyword_search: true
    });
}

// Balanced - good for most applications
function standardSearch(query, userId) {
    return client.search(query, {
        user_id: userId,
        keyword_search: true,
        rerank: true
    });
}

// High precision - good for critical applications  
function preciseSearch(query, userId) {
    return client.search(query, {
        user_id: userId,
        rerank: true,
        filter_memories: true
    });
}
```
</CodeGroup>

## Best Practices

### ✅ Do
- **Start simple** with just one enhancement and measure impact
- **Use keyword search** for entity-heavy queries (names, places, technical terms)
- **Use reranking** when the top result quality matters most
- **Use filtering** for production systems where precision is critical
- **Handle empty results** gracefully when filtering is too aggressive
- **Monitor latency** and adjust based on your application's needs

### ❌ Don't
- Enable all options by default without measuring necessity
- Use filtering for broad exploratory queries
- Ignore latency impact in real-time applications
- Forget to handle cases where filtering returns no results
- Use advanced retrieval for simple, fast lookup scenarios

## Performance Guidelines

### Latency Expectations

```python Python
# Performance monitoring example
import time

start_time = time.time()
results = client.search(
    query="user preferences",
    keyword_search=True,  # +10ms
    rerank=True,         # +150ms
    filter_memories=True, # +250ms
    user_id="user123"
)
latency = time.time() - start_time
print(f"Search completed in {latency:.2f}s")  # ~0.41s expected
```

### Optimization Tips

1. **Cache frequent queries** to avoid repeated advanced processing
2. **Use session-specific search** with `run_id` to reduce search space
3. **Implement fallback logic** when filtering returns empty results
4. **Monitor and alert** on search latency patterns

---

**Ready to enhance your search?** Start with keyword search for broader coverage, add reranking for better ordering, and use filtering when precision is critical.

<Snippet file="get-help.mdx" />
